import os
import asyncio
import logging
from typing import List, Dict, Any, Optional
from datetime import datetime

from .llm_service import LLMService

logger = logging.getLogger(__name__)

class RAGService:
    """RAG (检索增强生成) 服务 - 简化版本"""
    
    def __init__(self):
        self.llm_service = None
        self.is_initialized = False
        
        # 预构建知识库
        self.knowledge_base = self._build_knowledge_base()
    
    async def initialize(self):
        """初始化RAG服务"""
        try:
            logger.info("正在初始化RAG服务...")
            
            # 初始化LLM服务
            self.llm_service = LLMService()
            await self.llm_service.initialize()
            
            self.is_initialized = True
            logger.info("RAG服务初始化完成")
            
        except Exception as e:
            logger.error(f"RAG服务初始化失败: {e}")
            raise
    
    def _build_knowledge_base(self):
        """构建预定义知识库"""
        return {
            "transformer": {
                "title": "Attention Is All You Need",
                "authors": ["Vaswani, A.", "Shazeer, N.", "Parmar, N.", "Uszkoreit, J.", "Jones, L.", "Gomez, A. N.", "Kaiser, L.", "Polosukhin, I."],
                "year": 2017,
                "abstract": "提出了一种基于自注意力机制的神经网络架构，完全替代了循环和卷积神经网络。",
                "key_points": [
                    "自注意力机制允许模型关注输入序列中的不同位置",
                    "编码器-解码器架构支持并行计算",
                    "位置编码保持序列顺序信息"
                ],
                "citations": 80000,
                "arxiv_id": "1706.03762"
            },
            "bert": {
                "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                "authors": ["Devlin, J.", "Chang, M. W.", "Lee, K.", "Toutanova, K."],
                "year": 2018,
                "abstract": "提出了一种新的语言表示模型BERT，通过预训练深度双向Transformer在多个NLP任务上取得突破。",
                "key_points": [
                    "掩码语言模型实现双向上下文理解",
                    "预训练+微调的统一框架",
                    "在11个NLP任务上刷新记录"
                ],
                "citations": 50000,
                "arxiv_id": "1810.04805"
            },
            "gpt": {
                "title": "Improving Language Understanding by Generative Pre-Training",
                "authors": ["Radford, A.", "Narasimhan, K.", "Salimans, T.", "Sutskever, I."],
                "year": 2018,
                "abstract": "展示了生成式预训练在自然语言理解任务上的有效性。",
                "key_points": [
                    "无监督预训练+有监督微调",
                    "基于Transformer解码器的架构",
                    "在多个任务上优于专门模型"
                ],
                "citations": 30000,
                "arxiv_id": None
            },
            "resnet": {
                "title": "Deep Residual Learning for Image Recognition",
                "authors": ["He, K.", "Zhang, X.", "Ren, S.", "Sun, J."],
                "year": 2015,
                "abstract": "提出残差学习框架解决深度网络训练难题。",
                "key_points": [
                    "残差连接解决梯度消失问题",
                    "支持训练极深网络",
                    "在ImageNet等数据集上取得突破"
                ],
                "citations": 120000,
                "arxiv_id": "1512.03385"
            },
            "machine learning": {
                "title": "Machine Learning",
                "authors": ["Mitchell, T. M."],
                "year": 1997,
                "abstract": "机器学习的经典教材，系统介绍了机器学习的基本概念和方法。",
                "key_points": [
                    "监督学习、无监督学习、强化学习",
                    "特征工程和模型评估",
                    "过拟合与欠拟合问题"
                ],
                "citations": 50000,
                "arxiv_id": None
            }
        }
    
    async def search_documents(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:
        """基于关键词搜索相关文档"""
        try:
            query_lower = query.lower()
            matched_docs = []
            
            for topic, info in self.knowledge_base.items():
                if topic in query_lower:
                    matched_docs.append({
                        "content": f"标题: {info['title']}\n摘要: {info['abstract']}\n关键点: {'; '.join(info['key_points'])}",
                        "metadata": {
                            "title": info["title"],
                            "authors": info["authors"],
                            "arxiv_id": info["arxiv_id"],
                            "type": "paper"
                        }
                    })
            
            # 限制返回数量
            return matched_docs[:limit]
            
        except Exception as e:
            logger.error(f"搜索文档失败: {e}")
            return []
    
    async def generate_answer(
        self,
        question: str,
        context_papers: Optional[List[str]] = None,
        chat_history: Optional[List[Dict[str, str]]] = None
    ) -> Dict[str, Any]:
        """生成基于RAG的回答"""
        try:
            if not self.is_initialized:
                raise RuntimeError("RAG服务未初始化")
            
            # 检索相关文档
            docs = await self.search_documents(question, limit=3)
            
            # 构建上下文
            if docs:
                context = "\n\n".join([doc["content"] for doc in docs])
                sources = [{
                    "paper_id": doc["metadata"].get("arxiv_id", "unknown"),
                    "title": doc["metadata"].get("title", "未知标题"),
                    "excerpt": doc["content"][:200] + "..." if len(doc["content"]) > 200 else doc["content"],
                    "relevance_score": 0.9
                } for doc in docs]
                
                prompt = f"""基于以下上下文信息回答问题：

上下文信息:
{context}

问题: {question}

请提供详细且准确的回答："""
            else:
                context = "未找到相关上下文信息"
                sources = []
                prompt = question
            
            # 生成回答
            answer = await self.llm_service.generate_response(prompt)
            
            return {
                "answer": answer,
                "sources": sources,
                "confidence": 0.8 if docs else 0.5
            }
            
        except Exception as e:
            logger.error(f"生成回答失败: {e}")
            return {
                "answer": "抱歉，生成回答时出现错误。",
                "sources": [],
                "confidence": 0.0
            }
    
    async def add_document(self, content: str, metadata: Dict[str, Any]) -> bool:
        """添加文档到知识库（简化版本）"""
        try:
            logger.info(f"添加文档到知识库: {metadata.get('title', '未知标题')}")
            # 简化版本不实际存储文档
            return True
            
        except Exception as e:
            logger.error(f"添加文档失败: {e}")
            return False
    
    async def get_statistics(self) -> Dict[str, Any]:
        """获取统计信息"""
        try:
            return {
                "total_documents": len(self.knowledge_base),
                "knowledge_base_size": f"{len(str(self.knowledge_base))} characters",
                "last_updated": datetime.now().isoformat(),
                "model": "离线知识库模式"
            }
        except Exception as e:
            logger.error(f"获取统计信息失败: {e}")
            return {}
    
    def is_ready(self) -> bool:
        """检查服务是否就绪"""
        return self.is_initialized